{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "907a301a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from langchain.chat_models import init_chat_model\n",
    "os.environ[\"DEEPSEEK_API_KEY\"] = \"sk-9cc450ae42c04f3a8b4c46fc56f6d295\"\n",
    "llm  = init_chat_model(\"deepseek-chat\", model_provider=\"deepseek\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6e552061",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AIMessage(content='你好！很高兴见到你，有什么可以帮你的吗？😊', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 5, 'total_tokens': 19, 'completion_tokens_details': None, 'prompt_tokens_details': {'audio_tokens': None, 'cached_tokens': 0}, 'prompt_cache_hit_tokens': 0, 'prompt_cache_miss_tokens': 5}, 'model_name': 'deepseek-chat', 'system_fingerprint': 'fp_feb633d1f5_prod0820_fp8_kvcache', 'id': '3c661089-8ba9-44df-a40b-91e8464d1d66', 'service_tier': None, 'finish_reason': 'stop', 'logprobs': None}, id='run--83cf3ad4-eb9a-4a1f-9cf3-f0a73ce3fb9a-0', usage_metadata={'input_tokens': 5, 'output_tokens': 14, 'total_tokens': 19, 'input_token_details': {'cache_read': 0}, 'output_token_details': {}})"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "llm.invoke(\"你好\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e3f84cb0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File downloaded and saved as Chinook.db\n"
     ]
    }
   ],
   "source": [
    "import requests\n",
    "\n",
    "url = \"https://storage.googleapis.com/benchmarks-artifacts/chinook/Chinook.db\"\n",
    "response = requests.get(url)\n",
    "if response.status_code == 200:\n",
    "    # Open a local file in binary write mode\n",
    "    with open(\"Chinook.db\", \"wb\") as file:\n",
    "        # Write the content of the response (the file) to the local file\n",
    "        file.write(response.content)\n",
    "    print(\"File downloaded and saved as Chinook.db\")\n",
    "else:\n",
    "    print(f\"Failed to download the file. Status code: {response.status_code}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "02c1da14",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dialect: sqlite\n",
      "Available tables: ['Album', 'Artist', 'Customer', 'Employee', 'Genre', 'Invoice', 'InvoiceLine', 'MediaType', 'Playlist', 'PlaylistTrack', 'Track']\n",
      "Sample output: [(1, 'AC/DC'), (2, 'Accept'), (3, 'Aerosmith'), (4, 'Alanis Morissette'), (5, 'Alice In Chains')]\n"
     ]
    }
   ],
   "source": [
    "from langchain_community.utilities import SQLDatabase\n",
    "\n",
    "db = SQLDatabase.from_uri(\"sqlite:///Chinook.db\")\n",
    "\n",
    "print(f\"Dialect: {db.dialect}\")\n",
    "print(f\"Available tables: {db.get_usable_table_names()}\")\n",
    "print(f'Sample output: {db.run(\"SELECT * FROM Artist LIMIT 5;\")}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "92528b55",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sql_db_query: Input to this tool is a detailed and correct SQL query, output is a result from the database. If the query is not correct, an error message will be returned. If an error is returned, rewrite the query, check the query, and try again. If you encounter an issue with Unknown column 'xxxx' in 'field list', use sql_db_schema to query the correct table fields.\n",
      "\n",
      "sql_db_schema: Input to this tool is a comma-separated list of tables, output is the schema and sample rows for those tables. Be sure that the tables actually exist by calling sql_db_list_tables first! Example Input: table1, table2, table3\n",
      "\n",
      "sql_db_list_tables: Input is an empty string, output is a comma-separated list of tables in the database.\n",
      "\n",
      "sql_db_query_checker: Use this tool to double check if your query is correct before executing it. Always use this tool before executing a query with sql_db_query!\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from langchain_community.agent_toolkits import SQLDatabaseToolkit\n",
    "\n",
    "toolkit = SQLDatabaseToolkit(db=db, llm=llm)\n",
    "tools = toolkit.get_tools()\n",
    "\n",
    "for tool in tools:\n",
    "    print(f\"{tool.name}: {tool.description}\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8c9d60f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langgraph.prebuilt import create_react_agent\n",
    "\n",
    "system_prompt = \"\"\"\n",
    "使用中文\n",
    "You are an agent designed to interact with a SQL database.\n",
    "\n",
    "Given an input question, create a syntactically correct {dialect} query to run,\n",
    "then look at the results of the query and return the answer. Unless the user\n",
    "specifies a specific number of examples they wish to obtain, always limit your\n",
    "query to at most {top_k} results.\n",
    "\n",
    "You can order the results by a relevant column to return the most interesting\n",
    "examples in the database. Never query for all the columns from a specific table,\n",
    "only ask for the relevant columns given the question.\n",
    "\n",
    "You MUST double check your query before executing it. If you get an error while\n",
    "executing a query, rewrite the query and try again.\n",
    "\n",
    "DO NOT make any DML statements (INSERT, UPDATE, DELETE, DROP etc.) to the\n",
    "database.\n",
    "\n",
    "To start you should ALWAYS look at the tables in the database to see what you\n",
    "can query. Do NOT skip this step.\n",
    "\n",
    "Then you should query the schema of the most relevant tables.\n",
    "\"\"\".format(\n",
    "    dialect=db.dialect,\n",
    "    top_k=5,\n",
    ")\n",
    "\n",
    "agent = create_react_agent(\n",
    "    llm,\n",
    "    tools,\n",
    "    prompt=system_prompt,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7d8b8cb4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================\u001b[1m Human Message \u001b[0m=================================\n",
      "\n",
      "Which genre on average has the longest tracks?\n",
      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
      "\n",
      "I'll help you find which genre has the longest tracks on average. Let me start by exploring the database structure.\n",
      "Tool Calls:\n",
      "  sql_db_list_tables (call_00_rXnvQPHblyDbVh2ZOYr6OOSS)\n",
      " Call ID: call_00_rXnvQPHblyDbVh2ZOYr6OOSS\n",
      "  Args:\n",
      "    tool_input:\n",
      "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
      "Name: sql_db_list_tables\n",
      "\n",
      "Album, Artist, Customer, Employee, Genre, Invoice, InvoiceLine, MediaType, Playlist, PlaylistTrack, Track\n",
      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
      "\n",
      "Now let me look at the schema of the relevant tables - Genre and Track tables:\n",
      "Tool Calls:\n",
      "  sql_db_schema (call_00_TAKM1d4kV7IKQmyKkcjdGgXq)\n",
      " Call ID: call_00_TAKM1d4kV7IKQmyKkcjdGgXq\n",
      "  Args:\n",
      "    table_names: Genre, Track\n",
      "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
      "Name: sql_db_schema\n",
      "\n",
      "\n",
      "CREATE TABLE \"Genre\" (\n",
      "\t\"GenreId\" INTEGER NOT NULL, \n",
      "\t\"Name\" NVARCHAR(120), \n",
      "\tPRIMARY KEY (\"GenreId\")\n",
      ")\n",
      "\n",
      "/*\n",
      "3 rows from Genre table:\n",
      "GenreId\tName\n",
      "1\tRock\n",
      "2\tJazz\n",
      "3\tMetal\n",
      "*/\n",
      "\n",
      "\n",
      "CREATE TABLE \"Track\" (\n",
      "\t\"TrackId\" INTEGER NOT NULL, \n",
      "\t\"Name\" NVARCHAR(200) NOT NULL, \n",
      "\t\"AlbumId\" INTEGER, \n",
      "\t\"MediaTypeId\" INTEGER NOT NULL, \n",
      "\t\"GenreId\" INTEGER, \n",
      "\t\"Composer\" NVARCHAR(220), \n",
      "\t\"Milliseconds\" INTEGER NOT NULL, \n",
      "\t\"Bytes\" INTEGER, \n",
      "\t\"UnitPrice\" NUMERIC(10, 2) NOT NULL, \n",
      "\tPRIMARY KEY (\"TrackId\"), \n",
      "\tFOREIGN KEY(\"MediaTypeId\") REFERENCES \"MediaType\" (\"MediaTypeId\"), \n",
      "\tFOREIGN KEY(\"GenreId\") REFERENCES \"Genre\" (\"GenreId\"), \n",
      "\tFOREIGN KEY(\"AlbumId\") REFERENCES \"Album\" (\"AlbumId\")\n",
      ")\n",
      "\n",
      "/*\n",
      "3 rows from Track table:\n",
      "TrackId\tName\tAlbumId\tMediaTypeId\tGenreId\tComposer\tMilliseconds\tBytes\tUnitPrice\n",
      "1\tFor Those About To Rock (We Salute You)\t1\t1\t1\tAngus Young, Malcolm Young, Brian Johnson\t343719\t11170334\t0.99\n",
      "2\tBalls to the Wall\t2\t2\t1\tNone\t342562\t5510424\t0.99\n",
      "3\tFast As a Shark\t3\t2\t1\tF. Baltes, S. Kaufman, U. Dirkscneider & W. Hoffman\t230619\t3990994\t0.99\n",
      "*/\n",
      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
      "\n",
      "Now I'll create a query to find the average track length (in milliseconds) for each genre, ordered by the longest average length:\n",
      "Tool Calls:\n",
      "  sql_db_query_checker (call_00_S32SgsBU248LpBwhNKEkKYbp)\n",
      " Call ID: call_00_S32SgsBU248LpBwhNKEkKYbp\n",
      "  Args:\n",
      "    query: SELECT g.Name AS Genre, AVG(t.Milliseconds) AS AverageLength \n",
      "FROM Track t\n",
      "JOIN Genre g ON t.GenreId = g.GenreId\n",
      "GROUP BY g.Name\n",
      "ORDER BY AverageLength DESC\n",
      "LIMIT 5\n",
      "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
      "Name: sql_db_query_checker\n",
      "\n",
      "```sql\n",
      "SELECT g.Name AS Genre, AVG(t.Milliseconds) AS AverageLength \n",
      "FROM Track t\n",
      "JOIN Genre g ON t.GenreId = g.GenreId\n",
      "GROUP BY g.Name\n",
      "ORDER BY AverageLength DESC\n",
      "LIMIT 5\n",
      "```\n",
      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
      "Tool Calls:\n",
      "  sql_db_query (call_00_tWa0uOdOf9UDcGOIuRM8dqZ2)\n",
      " Call ID: call_00_tWa0uOdOf9UDcGOIuRM8dqZ2\n",
      "  Args:\n",
      "    query: SELECT g.Name AS Genre, AVG(t.Milliseconds) AS AverageLength \n",
      "FROM Track t\n",
      "JOIN Genre g ON t.Genid = g.GenreId\n",
      "GROUP BY g.Name\n",
      "ORDER BY AverageLength DESC\n",
      "LIMIT 5\n",
      "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
      "Name: sql_db_query\n",
      "\n",
      "Error: (sqlite3.OperationalError) no such column: t.Genid\n",
      "[SQL: SELECT g.Name AS Genre, AVG(t.Milliseconds) AS AverageLength \n",
      "FROM Track t\n",
      "JOIN Genre g ON t.Genid = g.GenreId\n",
      "GROUP BY g.Name\n",
      "ORDER BY AverageLength DESC\n",
      "LIMIT 5]\n",
      "(Background on this error at: https://sqlalche.me/e/20/e3q8)\n",
      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
      "\n",
      "I see there's a typo in the column name. Let me fix it:\n",
      "Tool Calls:\n",
      "  sql_db_query_checker (call_00_hpavjRtiRMFaI7FLyys2iy6Y)\n",
      " Call ID: call_00_hpavjRtiRMFaI7FLyys2iy6Y\n",
      "  Args:\n",
      "    query: SELECT g.Name AS Genre, AVG(t.Milliseconds) AS AverageLength \n",
      "FROM Track t\n",
      "JOIN Genre g ON t.GenreId = g.GenreId\n",
      "GROUP BY g.Name\n",
      "ORDER BY AverageLength DESC\n",
      "LIMIT 5\n",
      "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
      "Name: sql_db_query_checker\n",
      "\n",
      "```sql\n",
      "SELECT g.Name AS Genre, AVG(t.Milliseconds) AS AverageLength \n",
      "FROM Track t\n",
      "JOIN Genre g ON t.GenreId = g.GenreId\n",
      "GROUP BY g.Name\n",
      "ORDER BY AverageLength DESC\n",
      "LIMIT 5\n",
      "```\n",
      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
      "Tool Calls:\n",
      "  sql_db_query (call_00_PdGwu8yN4skxcKRkW5ABj9wU)\n",
      " Call ID: call_00_PdGwu8yN4skxcKRkW5ABj9wU\n",
      "  Args:\n",
      "    query: SELECT g.Name AS Genre, AVG(t.Milliseconds) AS AverageLength \n",
      "FROM Track t\n",
      "JOIN Genre g ON t.GenreId = g.GenreId\n",
      "GROUP BY g.Name\n",
      "ORDER BY AverageLength DESC\n",
      "LIMIT 5\n",
      "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
      "Name: sql_db_query\n",
      "\n",
      "[('Sci Fi & Fantasy', 2911783.0384615385), ('Science Fiction', 2625549.076923077), ('Drama', 2575283.78125), ('TV Shows', 2145041.0215053763), ('Comedy', 1585263.705882353)]\n",
      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
      "\n",
      "Based on the query results, the genre with the longest tracks on average is **Sci Fi & Fantasy** with an average track length of approximately 2,911,783 milliseconds (which is about 48.5 minutes).\n",
      "\n",
      "Here are the top 5 genres with the longest average track lengths:\n",
      "\n",
      "1. **Sci Fi & Fantasy** - 2,911,783 ms (≈48.5 minutes)\n",
      "2. **Science Fiction** - 2,625,549 ms (≈43.8 minutes)\n",
      "3. **Drama** - 2,575,284 ms (≈42.9 minutes)\n",
      "4. **TV Shows** - 2,145,041 ms (≈35.8 minutes)\n",
      "5. **Comedy** - 1,585,264 ms (≈26.4 minutes)\n",
      "\n",
      "These results suggest that genres like Sci Fi & Fantasy, Science Fiction, and Drama tend to have much longer tracks on average compared to other genres, likely because they include longer audio content like audiobooks, soundtracks, or extended musical compositions.\n"
     ]
    }
   ],
   "source": [
    "question = \"Which genre on average has the longest tracks?\"\n",
    "\n",
    "for step in agent.stream(\n",
    "    {\"messages\": [{\"role\": \"user\", \"content\": question}]},\n",
    "    stream_mode=\"values\",\n",
    "):\n",
    "    step[\"messages\"][-1].pretty_print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f5c01032",
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import Literal\n",
    "from langchain_core.messages import AIMessage\n",
    "from langchain_core.runnables import RunnableConfig\n",
    "from langgraph.graph import END, START, MessagesState, StateGraph\n",
    "from langgraph.prebuilt import ToolNode\n",
    "\n",
    "\n",
    "get_schema_tool = next(tool for tool in tools if tool.name == \"sql_db_schema\")\n",
    "get_schema_node = ToolNode([get_schema_tool], name=\"get_schema\")\n",
    "\n",
    "run_query_tool = next(tool for tool in tools if tool.name == \"sql_db_query\")\n",
    "run_query_node = ToolNode([run_query_tool], name=\"run_query\")\n",
    "\n",
    "\n",
    "# Example: create a predetermined tool call\n",
    "def list_tables(state: MessagesState):\n",
    "    tool_call = {\n",
    "        \"name\": \"sql_db_list_tables\",\n",
    "        \"args\": {},\n",
    "        \"id\": \"abc123\",\n",
    "        \"type\": \"tool_call\",\n",
    "    }\n",
    "    tool_call_message = AIMessage(content=\"\", tool_calls=[tool_call])\n",
    "\n",
    "    list_tables_tool = next(tool for tool in tools if tool.name == \"sql_db_list_tables\")\n",
    "    tool_message = list_tables_tool.invoke(tool_call)\n",
    "    response = AIMessage(f\"Available tables: {tool_message.content}\")\n",
    "\n",
    "    return {\"messages\": [tool_call_message, tool_message, response]}\n",
    "\n",
    "\n",
    "# Example: force a model to create a tool call\n",
    "def call_get_schema(state: MessagesState):\n",
    "    # Note that LangChain enforces that all models accept `tool_choice=\"any\"`\n",
    "    # as well as `tool_choice=<string name of tool>`.\n",
    "    llm_with_tools = llm.bind_tools([get_schema_tool], tool_choice=\"any\")\n",
    "    response = llm_with_tools.invoke(state[\"messages\"])\n",
    "\n",
    "    return {\"messages\": [response]}\n",
    "\n",
    "\n",
    "generate_query_system_prompt = \"\"\"\n",
    "You are an agent designed to interact with a SQL database.\n",
    "Given an input question, create a syntactically correct {dialect} query to run,\n",
    "then look at the results of the query and return the answer. Unless the user\n",
    "specifies a specific number of examples they wish to obtain, always limit your\n",
    "query to at most {top_k} results.\n",
    "\n",
    "You can order the results by a relevant column to return the most interesting\n",
    "examples in the database. Never query for all the columns from a specific table,\n",
    "only ask for the relevant columns given the question.\n",
    "\n",
    "DO NOT make any DML statements (INSERT, UPDATE, DELETE, DROP etc.) to the database.\n",
    "\"\"\".format(\n",
    "    dialect=db.dialect,\n",
    "    top_k=5,\n",
    ")\n",
    "\n",
    "\n",
    "def generate_query(state: MessagesState):\n",
    "    system_message = {\n",
    "        \"role\": \"system\",\n",
    "        \"content\": generate_query_system_prompt,\n",
    "    }\n",
    "    # We do not force a tool call here, to allow the model to\n",
    "    # respond naturally when it obtains the solution.\n",
    "    llm_with_tools = llm.bind_tools([run_query_tool])\n",
    "    response = llm_with_tools.invoke([system_message] + state[\"messages\"])\n",
    "\n",
    "    return {\"messages\": [response]}\n",
    "\n",
    "\n",
    "check_query_system_prompt = \"\"\"\n",
    "You are a SQL expert with a strong attention to detail.\n",
    "Double check the {dialect} query for common mistakes, including:\n",
    "- Using NOT IN with NULL values\n",
    "- Using UNION when UNION ALL should have been used\n",
    "- Using BETWEEN for exclusive ranges\n",
    "- Data type mismatch in predicates\n",
    "- Properly quoting identifiers\n",
    "- Using the correct number of arguments for functions\n",
    "- Casting to the correct data type\n",
    "- Using the proper columns for joins\n",
    "\n",
    "If there are any of the above mistakes, rewrite the query. If there are no mistakes,\n",
    "just reproduce the original query.\n",
    "\n",
    "You will call the appropriate tool to execute the query after running this check.\n",
    "\"\"\".format(dialect=db.dialect)\n",
    "\n",
    "\n",
    "def check_query(state: MessagesState):\n",
    "    system_message = {\n",
    "        \"role\": \"system\",\n",
    "        \"content\": check_query_system_prompt,\n",
    "    }\n",
    "\n",
    "    # Generate an artificial user message to check\n",
    "    tool_call = state[\"messages\"][-1].tool_calls[0]\n",
    "    user_message = {\"role\": \"user\", \"content\": tool_call[\"args\"][\"query\"]}\n",
    "    llm_with_tools = llm.bind_tools([run_query_tool], tool_choice=\"any\")\n",
    "    response = llm_with_tools.invoke([system_message, user_message])\n",
    "    response.id = state[\"messages\"][-1].id\n",
    "\n",
    "    return {\"messages\": [response]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9985251d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def should_continue(state: MessagesState) -> Literal[END, \"check_query\"]:\n",
    "    messages = state[\"messages\"]\n",
    "    last_message = messages[-1]\n",
    "    if not last_message.tool_calls:\n",
    "        return END\n",
    "    else:\n",
    "        return \"check_query\"\n",
    "\n",
    "\n",
    "builder = StateGraph(MessagesState)\n",
    "builder.add_node(list_tables)\n",
    "builder.add_node(call_get_schema)\n",
    "builder.add_node(get_schema_node, \"get_schema\")\n",
    "builder.add_node(generate_query)\n",
    "builder.add_node(check_query)\n",
    "builder.add_node(run_query_node, \"run_query\")\n",
    "\n",
    "builder.add_edge(START, \"list_tables\")\n",
    "builder.add_edge(\"list_tables\", \"call_get_schema\")\n",
    "builder.add_edge(\"call_get_schema\", \"get_schema\")\n",
    "builder.add_edge(\"get_schema\", \"generate_query\")\n",
    "builder.add_conditional_edges(\n",
    "    \"generate_query\",\n",
    "    should_continue,\n",
    ")\n",
    "builder.add_edge(\"check_query\", \"run_query\")\n",
    "builder.add_edge(\"run_query\", \"generate_query\")\n",
    "\n",
    "agent = builder.compile()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d05abaf5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import Image, display\n",
    "from langchain_core.runnables.graph import CurveStyle, MermaidDrawMethod, NodeStyles\n",
    "\n",
    "display(Image(agent.get_graph().draw_mermaid_png()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "acae89f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================\u001b[1m Human Message \u001b[0m=================================\n",
      "\n",
      "Which genre on average has the longest tracks?\n",
      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
      "\n",
      "Available tables: Album, Artist, Customer, Employee, Genre, Invoice, InvoiceLine, MediaType, Playlist, PlaylistTrack, Track\n",
      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
      "Tool Calls:\n",
      "  sql_db_schema (call_00_hoxbd9D9A1U0ppQ7zDlQjBt1)\n",
      " Call ID: call_00_hoxbd9D9A1U0ppQ7zDlQjBt1\n",
      "  Args:\n",
      "    table_names: Genre, Track\n",
      "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
      "Name: sql_db_schema\n",
      "\n",
      "\n",
      "CREATE TABLE \"Genre\" (\n",
      "\t\"GenreId\" INTEGER NOT NULL, \n",
      "\t\"Name\" NVARCHAR(120), \n",
      "\tPRIMARY KEY (\"GenreId\")\n",
      ")\n",
      "\n",
      "/*\n",
      "3 rows from Genre table:\n",
      "GenreId\tName\n",
      "1\tRock\n",
      "2\tJazz\n",
      "3\tMetal\n",
      "*/\n",
      "\n",
      "\n",
      "CREATE TABLE \"Track\" (\n",
      "\t\"TrackId\" INTEGER NOT NULL, \n",
      "\t\"Name\" NVARCHAR(200) NOT NULL, \n",
      "\t\"AlbumId\" INTEGER, \n",
      "\t\"MediaTypeId\" INTEGER NOT NULL, \n",
      "\t\"GenreId\" INTEGER, \n",
      "\t\"Composer\" NVARCHAR(220), \n",
      "\t\"Milliseconds\" INTEGER NOT NULL, \n",
      "\t\"Bytes\" INTEGER, \n",
      "\t\"UnitPrice\" NUMERIC(10, 2) NOT NULL, \n",
      "\tPRIMARY KEY (\"TrackId\"), \n",
      "\tFOREIGN KEY(\"MediaTypeId\") REFERENCES \"MediaType\" (\"MediaTypeId\"), \n",
      "\tFOREIGN KEY(\"GenreId\") REFERENCES \"Genre\" (\"GenreId\"), \n",
      "\tFOREIGN KEY(\"AlbumId\") REFERENCES \"Album\" (\"AlbumId\")\n",
      ")\n",
      "\n",
      "/*\n",
      "3 rows from Track table:\n",
      "TrackId\tName\tAlbumId\tMediaTypeId\tGenreId\tComposer\tMilliseconds\tBytes\tUnitPrice\n",
      "1\tFor Those About To Rock (We Salute You)\t1\t1\t1\tAngus Young, Malcolm Young, Brian Johnson\t343719\t11170334\t0.99\n",
      "2\tBalls to the Wall\t2\t2\t1\tNone\t342562\t5510424\t0.99\n",
      "3\tFast As a Shark\t3\t2\t1\tF. Baltes, S. Kaufman, U. Dirkscneider & W. Hoffman\t230619\t3990994\t0.99\n",
      "*/\n",
      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
      "\n",
      "Now I'll query to find which genre on average has the longest tracks by calculating the average track length (milliseconds) for each genre:\n",
      "Tool Calls:\n",
      "  sql_db_query (call_00_cunPkJXtiXZfiyOrPl2zEjrm)\n",
      " Call ID: call_00_cunPkJXtiXZfiyOrPl2zEjrm\n",
      "  Args:\n",
      "    query: SELECT g.Name as Genre, AVG(t.Milliseconds) as AverageTrackLength \n",
      "FROM Track t\n",
      "JOIN Genre g ON t.GenreId = g.GenreId\n",
      "GROUP BY g.Name\n",
      "ORDER BY AverageTrackLength DESC\n",
      "LIMIT 5\n",
      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
      "Tool Calls:\n",
      "  sql_db_query (call_00_J6vUmTI8o552bE9KkLLE9jEE)\n",
      " Call ID: call_00_J6vUmTI8o552bE9KkLLE9jEE\n",
      "  Args:\n",
      "    query: SELECT g.Name as Genre, AVG(t.Milliseconds) as AverageTrackLength \n",
      "FROM Track t\n",
      "JOIN Genre g ON t.GenreId = g.GenreId\n",
      "GROUP BY g.Name\n",
      "ORDER BY AverageTrackLength DESC\n",
      "LIMIT 5\n",
      "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
      "Name: sql_db_query\n",
      "\n",
      "[('Sci Fi & Fantasy', 2911783.0384615385), ('Science Fiction', 2625549.076923077), ('Drama', 2575283.78125), ('TV Shows', 2145041.0215053763), ('Comedy', 1585263.705882353)]\n",
      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
      "\n",
      "Based on the query results, the genre with the longest average track length is **Sci Fi & Fantasy** with an average track length of approximately 2,911,783 milliseconds (which is about 48.5 minutes).\n",
      "\n",
      "Here are the top 5 genres by average track length:\n",
      "\n",
      "1. **Sci Fi & Fantasy** - 2,911,783 ms (≈48.5 minutes)\n",
      "2. **Science Fiction** - 2,625,549 ms (≈43.8 minutes)\n",
      "3. **Drama** - 2,575,284 ms (≈42.9 minutes)\n",
      "4. **TV Shows** - 2,145,041 ms (≈35.8 minutes)\n",
      "5. **Comedy** - 1,585,264 ms (≈26.4 minutes)\n",
      "\n",
      "These results suggest that spoken word/audio book content (which would typically be found in genres like Sci Fi & Fantasy, Science Fiction, and Drama) tends to have much longer track durations compared to traditional music genres.\n"
     ]
    }
   ],
   "source": [
    "question = \"Which genre on average has the longest tracks?\"\n",
    "\n",
    "for step in agent.stream(\n",
    "    {\"messages\": [{\"role\": \"user\", \"content\": question}]},\n",
    "    stream_mode=\"values\",\n",
    "):\n",
    "    step[\"messages\"][-1].pretty_print()"
   ]
  }
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